High Resolution Melt Analysis (HRMA); a Viable Alternative to Agarose Gel Electrophoresis for Mouse Genotyping

Most mouse genetics laboratories maintain mouse strains that require genotyping in order to identify the genetically modified animals. The plethora of mutagenesis strategies and publicly available mouse alleles means that any one laboratory may maintain alleles with random or targeted insertions of orthologous or unrelated sequences as well as random or targeted deletions and point mutants. Many experiments require that different strains be cross bred conferring the need to genotype progeny at more than one locus. In contrast to the range of new technologies for mouse mutagenesis, genotyping methods have remained relatively static with alleles typically discriminated by agarose gel electrophoresis of PCR products. This requires a large amount of researcher time. Additionally it is susceptible to contamination of future genotyping experiments because it requires that tubes containing PCR products be opened for analysis. Progress has been made with the genotyping of mouse point mutants because a range of new high-throughput techniques have been developed for the detection of Single Nucleotide Polymorphisms. Some of these techniques are suitable for genotyping point mutants but do not detect insertion or deletion alleles. Ideally, mouse genetics laboratories would use a single, high-throughput platform that enables closed-tube analysis to genotype the entire range of possible insertion and deletion alleles and point mutants. Here we show that High Resolution Melt Analysis meets these criteria, it is suitable for closed-tube genotyping of all allele types and current genotyping assays can be converted to this technology with little or no effort.     

Variant Association Tools for Quality Control and Analysis of Large-Scale Sequence and Genotyping Array Data

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT''s data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., “exome chip,” data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.     

高分辨率熔解——SNP及突变研究的最新工具

随着分子生物学技术的不断发展,人们得以不断深入的研究单核苷酸多态性（SNP）以及突变与表型和疾病的相关性;相应的,人们对SNP及突变研究的关注也推动了相关研究手段的不断发展,使其在方法学上得以不断推陈出新。高分辨率熔解（high resolution melt,HRM）是近年来发展出的最新的SNP及突变研究工具,HRM因其高通量、低成本、不受检测位点的局限,且真正实现SNP的闭管检测而广受国外科研工作者的关注。该文对HRM技术与其他相关研究手段进行了比较,并对HRM技术的特点、应用进行了阐述。     

Evaluation of High Resolution Melting for MTHFR C677T Genotyping in Congenital Heart Disease

Background

High resolution melting (HRM) is a simple, flexible and low-cost mutation screening technique. The methylenetetrahydrofolate reductase (MTHFR) gene encoding a critical enzyme, potentially affects susceptibility to some congenital defects like congenital heart disease (CHD). We evaluate the performance of HRM for genotyping of the MTHFR gene C677T locus in CHD cases and healthy controls of Chinese Han population.

Methods

A total of 315 blood samples from 147 CHD patients (male72, female 75) and 168 healthy controls (male 92, female 76) were enrolled in the study. HRM was utilized to genotype MTHFR C677T locus of all the samples. The results were compared to that of PCR-RFLP and Sanger sequencing. The association of the MTHFR C677T genotypes and the risk of CHD was analyzed using odds ratio with their 95% confidence interval (CIs) from unconditional logistic regression.

Results

All the samples were successfully genotyped by HRM within 1 hour and 30 minutes while at least 6 hours were needed for PCR-RFLP and sequencing. The genotypes of MTHFR C677T CC, CT, and TT were 9.52%, 49.66%, and 40.82% in CHD group but 29.17%, 50% and 20.83% in control group, which were identical using both methods of HRM and PCR-RFLP, demonstrating the sensitivity and specificity of HRM were all 100%.

Conclusion

MTHFR C677T is a potential risk factor for CHD in our local residents of Shandong province in China. HRM is a fast, sensitive, specific and reliable method for clinical application of genotyping.     

Detection and Identification of Old World Leishmania by High Resolution Melt Analysis

Background

Three major forms of human disease, cutaneous leishmaniasis, visceral leishmaniasis and mucocutaneous leishmaniasis, are caused by several leishmanial species whose geographic distribution frequently overlaps. These Leishmania species have diverse reservoir hosts, sand fly vectors and transmission patterns. In the Old World, the main parasite species responsible for leishmaniasis are Leishmania infantum, L. donovani, L. tropica, L. aethiopica and L. major. Accurate, rapid and sensitive diagnostic and identification procedures are crucial for the detection of infection and characterization of the causative leishmanial species, in order to provide accurate treatment, precise prognosis and appropriate public health control measures.

Methods/Principal Findings

High resolution melt analysis of a real time PCR product from the Internal Transcribed Spacer-1 rRNA region was used to identify and quantify Old World Leishmania in 300 samples from human patients, reservoir hosts and sand flies. Different characteristic high resolution melt analysis patterns were exhibited by L. major, L. tropica, L. aethiopica, and L. infantum. Genotyping by high resolution melt analysis was verified by DNA sequencing or restriction fragment length polymorphism. This new assay was able to detect as little as 2-4 ITS1 gene copies in a 5 µl DNA sample, i.e., less than a single parasite per reaction.

Conclusions/Significance

This new technique is useful for rapid diagnosis of leishmaniasis and simultaneous identification and quantification of the infecting Leishmania species. It can be used for diagnostic purposes directly from clinical samples, as well as epidemiological studies, reservoir host investigations and vector surveys.     

Efficient Method of Genotyping Ob/Ob Mice Using High Resolution Melting Analysis

Objective

Direct health care costs of obesity continue to grow throughout the world and research on obesity disease models are on the rise. The ob/ob mouse is a well-characterized model of obesity and associated risk factors. Successful breeding and backcrossing onto different backgrounds are essential to create knockout models. Ob/ob mice are sterile and heterozygotes must be identified by genotyping to maintain breeding colonies. Several methods are employed to detect the ob mutant allele, a single nucleotide polymorphism (SNP). Gel based methods are time consuming and inconsistent, and non-gel based assays rely upon expensive and complex reagents or instruments. A fast, high-throughput, cost effective, and consistent method to identify Lep^ob mutation is much needed.

Design and Methods

Primers to produce an amplicon for High Resolution Melting Analysis (HRM) of the Lep^ob SNP were designed and validated.

Results

Fluorescence normalized high resolution melting curve plots delineated ob/+, ob/ob, and WT genotypes. Genotypes were also confirmed phenotypically.

Conclusions

HRM of the Lep^ob SNP allows closed-tube identification of the Lep^ob mutation using a real-time PCR machine now common to most labs/departments. Advantages of this method include assay sensitivity/accuracy, low cost dyes, less optimization, and cost effectiveness as compared to other genotyping techniques.     

Automated Classification and Cluster Visualization of Genotypes Derived from High Resolution Melt Curves

Introduction

High Resolution Melting (HRM) following PCR has been used to identify DNA genotypes. Fluorescent dyes bounded to double strand DNA lose their fluorescence with increasing temperature, yielding different signatures for different genotypes. Recent software tools have been made available to aid in the distinction of different genotypes, but they are not fully automated, used only for research purposes, or require some level of interaction or confirmation from an analyst.

Materials and Methods

We describe a fully automated machine learning software algorithm that classifies unknown genotypes. Dynamic melt curves are transformed to multidimensional clusters of points whereby a training set is used to establish the distribution of genotype clusters. Subsequently, probabilistic and statistical methods were used to classify the genotypes of unknown DNA samples on 4 different assays (40 VKORC1, CYP2C9*2, CYP2C9*3 samples in triplicate, and 49 MTHFR c.665C>T samples in triplicate) run on the Roche LC480. Melt curves of each of the triplicates were genotyped separately.

Results

Automated genotyping called 100% of VKORC1, CYP2C9*3 and MTHFR c.665C>T samples correctly. 97.5% of CYP2C9*2 melt curves were genotyped correctly with the remaining 2.5% given a no call due to the inability to decipher 3 melt curves in close proximity as either homozygous mutant or wild-type with greater than 99.5% posterior probability.

Conclusions

We demonstrate the ability to fully automate DNA genotyping from HRM curves systematically and accurately without requiring any user interpretation or interaction with the data. Visualization of genotype clusters and quantification of the expected misclassification rate is also available to provide feedback to assay scientists and engineers as changes are made to the assay or instrument.     

Improved Protocol for Rapid Identification of Certain Spa Types Using High Resolution Melting Curve Analysis

Methicillin-resistant Staphylococcus aureus is one of the most significant pathogens associated with health care. For efficient surveillance, control and outbreak investigation, S. aureus typing is essential. A high resolution melting curve analysis was developed and evaluated for rapid identification of the most frequent spa types found in an Austrian hospital consortium covering 2,435 beds. Among 557 methicillin-resistant Staphylococcus aureus isolates 38 different spa types were identified by sequence analysis of the hypervariable region X of the protein A gene (spa). Identification of spa types through their characteristic high resolution melting curve profiles was considerably improved by double spiking with genomic DNA from spa type t030 and spa type t003 and allowed unambiguous and fast identification of the ten most frequent spa types t001 (58%), t003 (12%), t190 (9%), t041 (5%), t022 (2%), t032 (2%), t008 (2%), t002 (1%), t5712 (1%) and t2203 (1%), representing 93% of all isolates within this hospital consortium. The performance of the assay was evaluated by testing samples with unknown spa types from the daily routine and by testing three different high resolution melting curve analysis real-time PCR instruments. The ten most frequent spa types were identified from all samples and on all instruments with 100% specificity and 100% sensitivity. Compared to classical spa typing by sequence analysis, this gene scanning assay is faster, cheaper and can be performed in a single closed tube assay format. Therefore it is an optimal screening tool to detect the most frequent endemic spa types and to exclude non-endemic spa types within a hospital.     

Development of 5123 Intron-Length Polymorphic Markers for Large-Scale Genotyping Applications in Foxtail Millet

Generating genomic resources in terms of molecular markers is imperative in molecular breeding for crop improvement. Though development and application of microsatellite markers in large-scale was reported in the model crop foxtail millet, no such large-scale study was conducted for intron-length polymorphic (ILP) markers. Considering this, we developed 5123 ILP markers, of which 4049 were physically mapped onto 9 chromosomes of foxtail millet. BLAST analysis of 5123 expressed sequence tags (ESTs) suggested the function for ∼71.5% ESTs and grouped them into 5 different functional categories. About 440 selected primer pairs representing the foxtail millet genome and the different functional groups showed high-level of cross-genera amplification at an average of ∼85% in eight millets and five non-millet species. The efficacy of the ILP markers for distinguishing the foxtail millet is demonstrated by observed heterozygosity (0.20) and Nei''s average gene diversity (0.22). In silico comparative mapping of physically mapped ILP markers demonstrated substantial percentage of sequence-based orthology and syntenic relationship between foxtail millet chromosomes and sorghum (∼50%), maize (∼46%), rice (∼21%) and Brachypodium (∼21%) chromosomes. Hence, for the first time, we developed large-scale ILP markers in foxtail millet and demonstrated their utility in germplasm characterization, transferability, phylogenetics and comparative mapping studies in millets and bioenergy grass species.     

PON-P2: Prediction Method for Fast and Reliable Identification of Harmful Variants

More reliable and faster prediction methods are needed to interpret enormous amounts of data generated by sequencing and genome projects. We have developed a new computational tool, PON-P2, for classification of amino acid substitutions in human proteins. The method is a machine learning-based classifier and groups the variants into pathogenic, neutral and unknown classes, on the basis of random forest probability score. PON-P2 is trained using pathogenic and neutral variants obtained from VariBench, a database for benchmark variation datasets. PON-P2 utilizes information about evolutionary conservation of sequences, physical and biochemical properties of amino acids, GO annotations and if available, functional annotations of variation sites. Extensive feature selection was performed to identify 8 informative features among altogether 622 features. PON-P2 consistently showed superior performance in comparison to existing state-of-the-art tools. In 10-fold cross-validation test, its accuracy and MCC are 0.90 and 0.80, respectively, and in the independent test, they are 0.86 and 0.71, respectively. The coverage of PON-P2 is 61.7% in the 10-fold cross-validation and 62.1% in the test dataset. PON-P2 is a powerful tool for screening harmful variants and for ranking and prioritizing experimental characterization. It is very fast making it capable of analyzing large variant datasets. PON-P2 is freely available at http://structure.bmc.lu.se/PON-P2/.     

Building a Hybrid Physical-Statistical Classifier for Predicting the Effect of Variants Related to Protein-Drug Interactions

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A New Microarray Substrate for Ultra-Sensitive Genotyping of KRAS and BRAF Gene Variants in Colorectal Cancer

Molecular diagnostics of human cancers may increase accuracy in prognosis, facilitate the selection of the optimal therapeutic regimen, improve patient outcome, reduce costs of treatment and favour development of personalized approaches to patient care. Moreover sensitivity and specificity are fundamental characteristics of any diagnostic method. We developed a highly sensitive microarray for the detection of common KRAS and BRAF oncogenic mutations. In colorectal cancer, KRAS and BRAF mutations have been shown to identify a cluster of patients that does not respond to anti-EGFR therapies; the identification of these mutations is therefore clinically extremely important. To verify the technical characteristics of the microarray system for the correct identification of the KRAS mutational status at the two hotspot codons 12 and 13 and of the BRAF^V600E mutation in colorectal tumor, we selected 75 samples previously characterized by conventional and CO-amplification at Lower Denaturation temperature-PCR (COLD-PCR) followed by High Resolution Melting analysis and direct sequencing. Among these samples, 60 were collected during surgery and immediately steeped in RNAlater while the 15 remainders were formalin-fixed and paraffin-embedded (FFPE) tissues. The detection limit of the proposed method was different for the 7 KRAS mutations tested and for the V600E BRAF mutation. In particular, the microarray system has been able to detect a minimum of about 0.01% of mutated alleles in a background of wild-type DNA. A blind validation displayed complete concordance of results. The excellent agreement of the results showed that the new microarray substrate is highly specific in assigning the correct genotype without any enrichment strategy.     

Development and Characterization of Simple Sequence Repeat Markers Providing Genome-Wide Coverage and High Resolution in Maize

Simple sequence repeats (SSRs) have been widely used in maize genetics and breeding, because they are co-dominant, easy to score, and highly abundant. In this study, we used whole-genome sequences from 16 maize inbreds and 1 wild relative to determine SSR abundance and to develop a set of high-density polymorphic SSR markers. A total of 264 658 SSRs were identified across the 17 genomes, with an average of 135 693 SSRs per genome. Marker density was one SSR every of 15.48 kb. (C/G)n, (AT)n, (CAG/CTG)n, and (AAAT/ATTT)n were the most frequent motifs for mono, di-, tri-, and tetra-nucleotide SSRs, respectively. SSRs were most abundant in intergenic region and least frequent in untranslated regions, as revealed by comparing SSR distributions of three representative resequenced genomes. Comparing SSR sequences and e-polymerase chain reaction analysis among the 17 tested genomes created a new database, including 111 887 SSRs, that could be develop as polymorphic markers in silico. Among these markers, 58.00, 26.09, 7.20, 3.00, 3.93, and 1.78% of them had mono, di-, tri-, tetra-, penta-, and hexa-nucleotide motifs, respectively. Polymorphic information content for 35 573 polymorphic SSRs out of 111 887 loci varied from 0.05 to 0.83, with an average of 0.31 in the 17 tested genomes. Experimental validation of polymorphic SSR markers showed that over 70% of the primer pairs could generate the target bands with length polymorphism, and these markers would be very powerful when they are used for genetic populations derived from various types of maize germplasms that were sampled for this study.     

A High Resolution Genome-Wide Scan for Significant Selective Sweeps: An Application to Pooled Sequence Data in Laying Chickens

In most studies aimed at localizing footprints of past selection, outliers at tails of the empirical distribution of a given test statistic are assumed to reflect locus-specific selective forces. Significance cutoffs are subjectively determined, rather than being related to a clear set of hypotheses. Here, we define an empirical p-value for the summary statistic by means of a permutation method that uses the observed SNP structure in the real data. To illustrate the methodology, we applied our approach to a panel of 2.9 million autosomal SNPs identified from re-sequencing a pool of 15 individuals from a brown egg layer line. We scanned the genome for local reductions in heterozygosity, suggestive of selective sweeps. We also employed a modified sliding window approach that accounts for gaps in the sequence and increases scanning resolution by moving the overlapping windows by steps of one SNP only, and suggest to call this a “creeping window” strategy. The approach confirmed selective sweeps in the region of previously described candidate genes, i.e. TSHR, PRL, PRLHR, INSR, LEPR, IGF1, and NRAMP1 when used as positive controls. The genome scan revealed 82 distinct regions with strong evidence of selection (genome-wide p-value<0.001), including genes known to be associated with eggshell structure and immune system such as CALB1 and GAL cluster, respectively. A substantial proportion of signals was found in poor gene content regions including the most extreme signal on chromosome 1. The observation of multiple signals in a highly selected layer line of chicken is consistent with the hypothesis that egg production is a complex trait controlled by many genes.     

Comparison of Eleven Methods for Genomic DNA Extraction Suitable for Large-Scale Whole-Genome Genotyping and Long-Term DNA Banking Using Blood Samples

Over the recent years, next generation sequencing and microarray technologies have revolutionized scientific research with their applications to high-throughput analysis of biological systems. Isolation of high quantities of pure, intact, double stranded, highly concentrated, not contaminated genomic DNA is prerequisite for successful and reliable large scale genotyping analysis. High quantities of pure DNA are also required for the creation of DNA-banks. In the present study, eleven different DNA extraction procedures, including phenol-chloroform, silica and magnetic beads based extractions, were examined to ascertain their relative effectiveness for extracting DNA from ovine blood samples. The quality and quantity of the differentially extracted DNA was subsequently assessed by spectrophotometric measurements, Qubit measurements, real-time PCR amplifications and gel electrophoresis. Processing time, intensity of labor and cost for each method were also evaluated. Results revealed significant differences among the eleven procedures and only four of the methods yielded satisfactory outputs. These four methods, comprising three modified silica based commercial kits (Modified Blood, Modified Tissue, Modified Dx kits) and an in-house developed magnetic beads based protocol, were most appropriate for extracting high quality and quantity DNA suitable for large-scale microarray genotyping and also for long-term DNA storage as demonstrated by their successful application to 600 individuals.     

Development and Application of a Whole-Genome Simple Sequence Repeat Panel for High-Throughput Genotyping in Soybean

Among commonly applied molecular markers, simple sequence repeats (SSRs, or microsatellites) possess advantages such as a high level of polymorphism and codominant pattern of inheritance at individual loci. To facilitate systematic and rapid genetic mapping in soybean, we designed a genotyping panel comprised 304 SSR markers selected for allelic diversity and chromosomal location so as to provide wide coverage. Most primer pairs for the markers in the panel were redesigned to yield amplicons of 80–600 bp in multiplex polymerase chain reaction (PCR) and fluorescence-based sequencer analysis, and they were labelled with one of four different fluorescent dyes. Multiplex PCR with sets of six to eight primer pairs per reaction generated allelic data for 283 of the 304 SSR loci in three different mapping populations, with the loci mapping to the same positions as previously determined. Four SSRs on each chromosome were analysed for allelic diversity in 87 diverse soybean germplasms with four-plex PCR. These 80 loci showed an average allele number and polymorphic information content value of 14.8 and 0.78, respectively. The high level of polymorphism, ease of analysis, and high accuracy of the SSR genotyping panel should render it widely applicable to soybean genetics and breeding.     

Method for Improving the Reliability of Learning Machine

Optimization of learning machine prediction has been attempted by the addition of a “width” parameter proposed by Wangen et al. Predictions were made for the production of phenazine compounds, the utilizations of d-sorbitol, of n-hexadecane, and of histamine by pseudomonads. In all but one cases examined, the “stability” of prediction was much improved when a high positive value was assigned to the parameter. A slight increase was often found in the hit-to-miss ratio, but the χ² test revealed that it was statistically insignificant.